Our aim is to build an unsupervised and language-independent computational model which is able to distinguish metaphoric change from semantic stability. We apply entropy (a measure of uncertainty inherited from information theory) to a Distributional Semantic Model (DSM). In particular, we exploit the idea of semantic generality applied in hypernym detection, to detect metaphoric change as a special process of meaning innovation. German will serve as a sample language, since there is a rich historical corpus available covering a large time period. Nevertheless, our model is presumably applicable to other languages requiring only minor adjustments. With the model, we introduce the first resource for evaluation of models of metaphoric change and propose a structured annotation process that is generalizable to the creation of gold standards for other types of semantic change.The test set is provided together with the annotation data and the model code (which is based on [1]}'s code): https://github.com/Garrafao/MetaphoricChange
